This study explores the use of Google Trends data to quantify trading behavior in financial markets. By analyzing changes in search volumes for financial terms, the researchers identify patterns that may serve as early warning signs of stock market movements. The study suggests that Google Trends data can provide insights into the information-gathering behavior of market participants, potentially aiding in the development of profitable trading strategies. The researchers analyzed 98 search terms related to financial markets, including terms suggested by Google's semantic keyword tool. They found that search volume data for terms like "debt" correlated with stock market movements. By comparing search volume data with stock market prices, they developed a "Google Trends strategy" that involved buying or selling stocks based on changes in search volume. This strategy showed significant returns, with a 326% increase in portfolio value over the period 2004–2011. The study also found that strategies based on U.S. search volume data were more successful for the U.S. market than those based on global data. This is attributed to the higher proportion of traders among U.S. Internet users. The results support the hypothesis that changes in search volume for financially relevant terms can predict stock market movements, with increases in search volume preceding market declines and decreases preceding market rises. The study highlights the potential of combining large behavioral data sets, such as financial trading data and search query volumes, to gain new insights into collective decision-making processes. The findings suggest that Google Trends data can provide valuable information about the behavior of economic actors, potentially enhancing our understanding of complex collective behavior in society. The study also emphasizes the need for further research into the psychological mechanisms underlying such behaviors.This study explores the use of Google Trends data to quantify trading behavior in financial markets. By analyzing changes in search volumes for financial terms, the researchers identify patterns that may serve as early warning signs of stock market movements. The study suggests that Google Trends data can provide insights into the information-gathering behavior of market participants, potentially aiding in the development of profitable trading strategies. The researchers analyzed 98 search terms related to financial markets, including terms suggested by Google's semantic keyword tool. They found that search volume data for terms like "debt" correlated with stock market movements. By comparing search volume data with stock market prices, they developed a "Google Trends strategy" that involved buying or selling stocks based on changes in search volume. This strategy showed significant returns, with a 326% increase in portfolio value over the period 2004–2011. The study also found that strategies based on U.S. search volume data were more successful for the U.S. market than those based on global data. This is attributed to the higher proportion of traders among U.S. Internet users. The results support the hypothesis that changes in search volume for financially relevant terms can predict stock market movements, with increases in search volume preceding market declines and decreases preceding market rises. The study highlights the potential of combining large behavioral data sets, such as financial trading data and search query volumes, to gain new insights into collective decision-making processes. The findings suggest that Google Trends data can provide valuable information about the behavior of economic actors, potentially enhancing our understanding of complex collective behavior in society. The study also emphasizes the need for further research into the psychological mechanisms underlying such behaviors.